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Accounting for the Spatio-Temporal Variability of Pollutant Processes in Stormwater TSS Modeling Based on Stochastic Approaches

1
LEESU, MA 102, Ecole des Ponts, AgroParisTech, UPEC, UPE, 77420 Champs-sur-Marne, France
2
Laboratoire Eau et Environnement, GERS, Institut Français des Sciences et Technologies des Transports, de l’Aménagement et des Réseaux (IFSTTAR), 44340 F-Bouguenais, France
3
Ministry for an Ecological and Inclusive Transition, 92055 Paris-La-Défense Cedex, France
4
Faculty of Engineering III, Lebanese University, 1003 Hadath, Lebanon
*
Author to whom correspondence should be addressed.
Water 2018, 10(12), 1773; https://doi.org/10.3390/w10121773
Received: 30 September 2018 / Revised: 26 November 2018 / Accepted: 29 November 2018 / Published: 3 December 2018
(This article belongs to the Special Issue GIS-Based Hydrology and Water Quality Modeling)
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Abstract

Stormwater quality modeling remains one of the most challenging issues in urban hydrology today. The processes involved in contaminant generation and transport are very complex, with many associated uncertainties, including uncertainty arising from process variability. In this study, the spatio-temporal variability of build-up/wash-off processes in a heterogeneous urban catchment within the Parisian region is assessed based on three stochastic modeling approaches integrated into the physically based distributed hydrological model, the Urban Runoff Branching Structure (URBS) model. Results demonstrate that accounting for process variability at the scale of a hydrological element is important for analyzing the contamination recorded at the catchment outlet. The intra-event dynamics of total suspended solids (TSS) were most accurately selected for the stochastic exponential SWMM model, as this model succeeded not only in simulating the general trend of TSS concentrations fluctuations but also in replicating multiple peaks observed in pollutographs. The advantage of this approach is that it captures the stochastic nature of the processes with minimal prior knowledge and without extensive calibration, though further enhancement is necessary for it to become a useful tool to support decision making. View Full-Text
Keywords: stochastic approach; process variability; stormwater runoff; modeling; pollutant wash-off; TSS stochastic approach; process variability; stormwater runoff; modeling; pollutant wash-off; TSS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Al Ali, S.; Rodriguez, F.; Bonhomme, C.; Chebbo, G. Accounting for the Spatio-Temporal Variability of Pollutant Processes in Stormwater TSS Modeling Based on Stochastic Approaches. Water 2018, 10, 1773.

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